---
title: 'SR-Platform: Autonomous Scene Synthesis'
url: https://www.emergentmind.com/topics/sr-platform
type: topic
---

# SR-Platform: Autonomous Scene Synthesis

SR-Platform refers to a production-deployed, modular, agentic pipeline that converts free-form natural language descriptions of robot workspaces into fully executable, physically validated MuJoCo environments. SR-Platform automates the conversion of plain-text prompts into MuJoCo (MJCF) XML scene specifications, including robot models, 3D object meshes, spatial layouts, collision checking, and safety-rule enforcement, making it a key system for simulation-based robot learning and synthetic data generation [2605.14700].

## 1. Architectural Overview

SR-Platform consists of a four-stage pipeline, supported by a nine-service Dockerized backend and a web-based interactive frontend. The architecture is natively agentic, decomposing environment synthesis into modular services:

1. **L1 Orchestrator (Scene Planner):**  
   - Receives free-form English prompts via the frontend.  
   - Utilizes a LangGraph-based workflow, with a single LLM call structured to produce a normalized JSON "scene plan," which is then validated against a schema.  
   - Output includes room geometry, robot identity, a list of N semantic object types (e.g., "lab_bench," "optical_microscope"), and configuration options.

2. **L2 Asset Forge (Mesh Resolver):**
   - For each object label, computes a text embedding and queries the Qdrant vector DB for cached STL meshes.  
   - On cache miss, invokes an LLM to generate CadQuery code for 3D mesh synthesis (automatic code execution, mesh validation, STL upload to MinIO, caching of new embeddings).
   - Employs an automatic retry mechanism with a first-attempt retry rate of 11.3% for mesh generation.

3. **L3 Layout Architect (Pose Assignment & Safety Checker):**
   - Assigns poses to each mesh instance, enforcing collision-free layouts using AABB overlap checks.  
   - Integrates industry-specific rule checkers (clearances, fire egress, machine-robot distance, airflow requirements), with automatic resampling or anomaly marking.

4. **L4 Bridge & MJCF Assembly (Scene Builder):**
   - Collects prior outputs and generates deterministic MJCF XML with all asset, body, geom, and robot integration details.
   - Robot models (e.g., "ur5") are merged using a registry and insertion policy consistent with the workspace context.

The entire system operates as an asynchronous actor pipeline interconnected by Redis-backed job queues, WebSockets for real-time streaming, and REST/gRPC/S3 connections to Qdrant, MinIO, PostgreSQL, and InfluxDB for asset, job state, and telemetry management.

## 2. Software Stack and Workflow

The platform is composed of the following core services, each containerized:

| Service Name         | Function                                           | Key Interfaces                           |
|---------------------|----------------------------------------------------|------------------------------------------|
| Frontend            | Prompt entry, MJCF visualization, progress display | React, MuJoCo-WASM, WebSocket            |
| Backend API         | REST/WebSocket, user and job state                 | FastAPI, Redis, PostgreSQL               |
| Worker Pool         | L1–L4 pipeline execution                           | ARQ for task queueing                    |
| Qdrant              | Semantic mesh cache/index                          | gRPC API                                 |
| MinIO               | STL mesh artefact storage                          | S3 HTTP                                  |
| PostgreSQL          | Metadata, user, asset storage                      | SQL                                      |
| Redis               | Job queue, per-user scene state                    | Redis                                    |
| InfluxDB            | Telemetry: job, LLM timings, retry statistics      | Time-series API                          |
| pgAdmin             | Database management                                | Web UI                                   |

Prompt flow:  
User submits prompt → Backend (enqueue job in Redis) → Worker executes L1–L4 (calls Qdrant and MinIO, records to InfluxDB and PostgreSQL) → Progress/report events streamed via WebSocket → Final MJCF artefact pulled by frontend, rendered with MuJoCo-WASM.

## 3. Performance Metrics and Telemetry

SR-Platform has been production-evaluated over 30 days (611 successful LLM calls). Key quantitative findings [2605.14700]:

- **End-to-end latency:**  
  - Median ≈ 50 s for five-object scenes (all cache miss).  
  - Cache-accelerated scenes (all cache hits) complete in ≈ 30–40 s.
- **Latency breakdown:**  
  $$ T_{\mathrm{total}} = T_{\mathrm{orchestrator}} + \sum_{i=1}^N (T_{\mathrm{asset\,forge},\,i} + T_{\mathrm{layout},\,i}) + T_{\mathrm{bridge}} $$
  - $T_{\mathrm{orchestrator}} \approx 16\ \text{s}$ (median)
  - $T_{\mathrm{asset\,forge},\,i} \approx 17.9\ \text{s}$ LLM + CadQuery, $0\ \text{s}$ (cache hit)
  - $T_{\mathrm{layout},\,i} \approx 16\ \text{s}$
  - $T_{\mathrm{bridge}} < 1\ \text{s}$
- **First-attempt retry rate:**  
  $$ \text{retry\_rate} = \frac{\text{failed first attempts}}{\text{total asset requests}} = \frac{34}{300} \approx 11.3\% $$
- **Skill generator (L2), p50 = 17.9 s, p95 = 65.4 s**;  
  **Layout/unknown (L1+L3), p50 = 15.9 s**.

Caches eliminated LLM overhead for previously generated object types, underscoring production suitability for interactive robot-scene generation.

## 4. Scene Synthesis Pipeline: Data Flow and Example

A natural-language prompt, such as "a small laboratory scene: lab bench under a window, optical microscope on the bench, pipette rack and beaker next to it, rolling stool in front, and a UR5 robot mounted at bench’s left end", is processed as follows:

1. **

Source: https://www.emergentmind.com/topics/sr-platform